Basic Machine Learning Flashcards
Algorithm
Step-by-step procedure designed to carry out a task.
Change detection
Identifying when a significant change has taken place in a process.
Classification
The separation of data into two or more categories, or (a point’s
classification) the category a data point is put into.
Classifier
A boundary that separates the data into two or more categories. Also
(more generally) an algorithm that performs classification.
Cluster
A group of points identified as near/similar to each other.
Cluster center
In some clustering algorithms (like 𝑘𝑘-means clustering), the central
point (often the centroid) of a cluster of data points.
Clustering
Separation of data points into groups (“clusters”) based on
nearness/similarity to each other. A common form of unsupervised
learning
CUSUM
Change detection method that compares observed distribution mean
with a threshold level of change. Short for “cumulative sum”.
Deep learning
Neural network-type model with many hidden layers.
Dimension
A feature of the data points (for example, height or credit score). (Note that there is also a mathematical definition for this word.)
EM algorithm
Expectation-maximization algorithm.
Expectation-maximization
algorithm (EM algorithm)
General description of an algorithm with two steps (often iterated), one that finds the function for the expected likelihood of getting the response given current parameters, and one that finds new parameter
values to maximize that probability.
Heuristic
Algorithm that is not guaranteed to find the absolute best (optimal) solution.
𝑘-means algorithm
Clustering algorithm that defines 𝑘 clusters of data points, each
corresponding to one of 𝑘 cluster centers selected by the algorithm.
𝑘-Nearest-Neighbor (KNN)
Classification algorithm that defines a data point’s category as a function of the nearest 𝑘 data points to it.
Kernel
A type of function that computes the similarity between two inputs; thanks to what’s (really!) sometimes known as the “kernel trick”, nonlinear classifiers can be found almost as easily as linear ones.
Learning
Finding/discovering patterns (or rules) in data, often that can be applied to new data.
Machine
Apparatus that can do something; in “machine learning”, it often refers to both an algorithm and the computer it’s run on. (Fun fact: before
computers were developed, the term “computers” referred to people who did calculations quickly in their heads or on paper!)
Margin
For a single point, the distance between the point and the classification boundary; for a set of points, the minimum distance between a point in the set and the classification boundary. Also called the separation.
Machine learning
Use of computer algorithms to learn and discover patterns or structure in data, without being programmed specifically for them.
Misclassified
Put into the wrong category by a classifier.
Neural network
A machine learning model that itself is modeled after the workings of neurons in the brain.
Supervised learning
Machine learning where the “correct” answer is known for each data point in the training set.
Support vector
In SVM models, the closest point to the classifier, among those in a category. (Note that there is a more-technical mathematical definition too.)
Support vector machine (SVM)
Classification algorithm that uses a boundary to separate the data into two or more categories (“classes”).
SVM
Support vector machine.
Unsupervised learning
Machine learning where the “correct” answer is not known for the data points in the training set.
Voronoi diagram
Graphical representation of splitting a plane with two or more special points into regions with one special point each, where each region’s points are closer to the region’s special point than to any other special
point.
